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Verify deployment

verify_deployment
Read-onlyIdempotent

Run post-deployment checks: workload exists, ready replicas match desired, pods Running/Ready with zero restarts, services have endpoints, optional health endpoint responds. If unhealthy, diagnose.

Instructions

Run the post-deployment checks in one call: the workload exists, ready replicas match desired, pods are Running and Ready, restart counts are zero, Services exist and have endpoints, and optionally that a health endpoint responds through the API server proxy. Never assume a deployment succeeded -- call this. If healthy is false, call diagnose_deployment.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
namesNo
namespaceNo
health_pathNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
podsNo
checksNo
healthyYes
summaryNo
servicesNo
namespaceYes
next_stepNo
deploymentsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.4/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare read-only, idempotent, non-destructive behavior, and the description adds substantial context beyond that: the specific checks performed, health-check proxying, and the existence of a 'healthy' boolean result. There is no contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but efficient: the core behavior is front-loaded, each clause adds a distinct check, and the routing instruction earns its place. No redundant or filler content.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers purpose, behavior, output signal, and escalation, and an output schema exists to handle return details. The only notable gap is the meaning and selection semantics of names and namespace, which keeps it from being fully complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It clarifies health_path ('health endpoint responds through the API server proxy') but does not explain how names or namespace select workloads, nor what null defaults mean. This is a meaningful gap for optional parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific verb and resource: 'Run the post-deployment checks in one call' and enumerates exactly which checks are performed. It also implicitly differentiates this aggregated verification tool from the individual sibling status tools like get_pods and get_services.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicit guidance is given: 'Never assume a deployment succeeded -- call this.' It also names the escalation path: 'If healthy is false, call diagnose_deployment.' This tells the agent when to invoke this tool versus alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.